Liz Wright is a Director Analyst at Gartner, covering digital transformation, AI enablement, and modern data strategy within R&D environments. Drawing from extensive practitioner experience modernizing scientific research workflows, Liz advises senior R&D, technology, and analytics executives on turning emerging technologies into measurable scientific and business outcomes. Her research centers on pragmatic AI adoption, modular lab architectures, and data-centric operating models that streamline research decision-making and reduce cognitive load for scientists.
Prior to Gartner, Liz served as Digital Transformation Lead for Small Molecules R&D at Bayer Crop Science, leading late-stage R&D digital initiatives across global research organizations. At Bayer, she shaped strategy for AI-enabled workflows, advanced enterprise perspectives on "no-LIMS" and composable lab data architectures, and designed GenAI enablement programs for scientific staff. Her background combines hands-on laboratory operations with enterprise strategy, giving her deep domain credibility in translating complex tech into executive action.
Bayer, Transformation Lead, 5 years
Bayer, Digital Lead, 3 years
Bayer/Monsanto, Demand Planning Lead, 4 years
Monsanto, Pipeline Manger, 8 years
AI, Automation and Emerging Technology Management
Emerging Technology Adoption
Life Sciences Industry Technology Management
AI Strategy and Business Transformation
AI and Digital Technology Integration
Digital Transformation Certificate, MIT Professional Education
Executive MBA, Quantic School of Business and Technology
B.S. in Biology and Sociology, Gonzaga University
Transitioning R&D-IT governance from an IT-led software deployment model to a joint operating strategy that safeguards scientific context and enterprise value.
Shifting executive focus from application acquisition to building enduring, vendor-neutral data assets while treating software applications as swappable, disposable utilities.
Leading the cultural reset required to move organizational status from bench-first trial-and-error to computational, in-silico-first discovery.
Neutralizing systemic adoption resistance and status insecurity across key lab profiles—from senior technical leaders to bench staff—during enterprise digital transformations.
Accelerating legacy application exits and protecting crown-jewel intellectual property by bypassing multi-year database re-mapping bottlenecks.